Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data

An Min Tang, Xingqiu Zhao, Nian Sheng Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


KGaA, Weinheim This paper presents a novel semiparametric joint model for multivariate longitudinal and survival data (SJMLS) by relaxing the normality assumption of the longitudinal outcomes, leaving the baseline hazard functions unspecified and allowing the history of the longitudinal response having an effect on the risk of dropout. Using Bayesian penalized splines to approximate the unspecified baseline hazard function and combining the Gibbs sampler and the Metropolis–Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates in SJMLS. Simulation studies are conducted to investigate the finite sample performance of the proposed techniques. An example from the International Breast Cancer Study Group (IBCSG) is used to illustrate the proposed methodologies.
Original languageEnglish
Pages (from-to)57-78
Number of pages22
JournalBiometrical Journal
Issue number1
Publication statusPublished - 1 Jan 2017


  • Bayesian Lasso
  • Bayesian penalized splines
  • Joint models
  • Mixture of normals
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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